Image segmentation is a branch of digital image processing for categorizing or classifying the picture elements of a digital image as belonging or associated with one or more class types. For medical imaging applications, image segmentation is commonly performed on the voxels (volume elements) of a 3-dimensional image data set with the classification types relating to various anatomical structures. In thoracic medical images, for example, it is convenient to segment the image voxels into classes such as bone, lung parenchyma, soft tissue, bronchial vessels, blood vessels, etc. There are many reasons to perform such a task, such as for surgical planning, treatment progress, and patient diagnosis.
Among image segmentation approaches is the technique generally known as region growing. Region growing begins by identifying a seed point, i.e., a voxel position that is known to be part of a particular class type. A region of voxels, often contiguous in nature, is then grown or otherwise developed about the seed point. The region growing process progresses until a terminating condition is satisfied, e.g., no more voxels that meet suitable criteria are found, or a predetermined number of voxels have been visited, etc. Conventional segmentation approaches that begin with a seed point have been found to work acceptably for some types of organ segmentation problems.
The liver, because of the relatively complex tissue structure of this organ, proves to be particularly challenging for conventional segmentation approaches. One conventional technique is described in a paper entitled “An iterative Bayesian approach for nearly automatic liver segmentation: algorithm and validation” in International Journal of Computer Assisted Radiology and Surgery, Volume 3, Number 5, November 2008 by Freiman et al. These authors describe an algorithm for segmenting the liver organ in thoracic CT (computed tomography) medical volume images.
Applicants have noted that noise levels in the CT image make this type of approach unusable.
The method described by Rudin, Osher, and Fatemi in the article “Nonlinear total variation based noise removal algorithms” published in Proceedings of the Eleventh Annual International Conference of the Center for Nonlinear Studies on Experimental Mathematics: Computational Issues in Nonlinear Science (1992), pp. 259-268 attempts to compensate for noise in the CT image. However, the resulting liver segmentation maps produced can be over-segmented, often including substantial regions of non-liver tissue.
A contrast agent can be administered to the patient, and can be relevant to the imaging processing algorithms for segmentation of the liver and other organs in CT exam images.
The information that is available from segmentation of the liver and other organs includes information on the overall volume of the organ. Quantification of liver volume can be particularly valuable information for the diagnostician. The size of the liver can change over a period of time and can indicate various disease conditions, for example. However, computation of the liver volume requires accurate segmentation and it has proved difficult to obtain repeatable results. One problem that affects volume computation relates to image contrast and the contrast-to-noise ratio (CNR). Segmentation can have less accuracy where contrast is poor. Even where a contrast agent is used, volume computation can be difficult and results less accurate, since the most useful series of images of the liver, in a particular case, may not be those for which contrast is enhanced using the contrast agent.
Thus, it can be appreciated that there is a need for an improved image processing method for segmentation of the liver and other organs and for volume computation based on the achieved segmentation.